741 research outputs found
Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services
Through-life Engineering Services (TES) are essential in the manufacture and servicing of complex engineering products. TES improves support services by providing prognosis of run-to-failure and time-to-failure on-demand data for better decision making. The concept of Remaining Useful Life (RUL) is utilised to predict life-span of components (of a service system) with the purpose of minimising catastrophic failure events in both manufacturing and service sectors. The purpose of this paper is to identify failure mechanisms and emphasise the failure events prediction approaches that can effectively reduce uncertainties. It will demonstrate the classification of techniques used in RUL prediction for optimisation of products’ future use based on current products in-service with regards to predictability, availability and reliability. It presents a mapping of degradation mechanisms against techniques for knowledge acquisition with the objective of presenting to designers and manufacturers ways to improve the life-span of components
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
In recent times, Large Language Models (LLMs) have captured a global
spotlight and revolutionized the field of Natural Language Processing. One of
the factors attributed to the effectiveness of LLMs is the model architecture
used for training, transformers. Transformer models excel at capturing
contextual features in sequential data since time series data are sequential,
transformer models can be leveraged for more efficient time series data
prediction. The field of prognostics is vital to system health management and
proper maintenance planning. A reliable estimation of the remaining useful life
(RUL) of machines holds the potential for substantial cost savings. This
includes avoiding abrupt machine failures, maximizing equipment usage, and
serving as a decision support system (DSS). This work proposed an
encoder-transformer architecture-based framework for multivariate time series
prediction for a prognostics use case. We validated the effectiveness of the
proposed framework on all four sets of the C-MAPPS benchmark dataset for the
remaining useful life prediction task. To effectively transfer the knowledge
and application of transformers from the natural language domain to time
series, three model-specific experiments were conducted. Also, to enable the
model awareness of the initial stages of the machine life and its degradation
path, a novel expanding window method was proposed for the first time in this
work, it was compared with the sliding window method, and it led to a large
improvement in the performance of the encoder transformer model. Finally, the
performance of the proposed encoder-transformer model was evaluated on the test
dataset and compared with the results from 13 other state-of-the-art (SOTA)
models in the literature and it outperformed them all with an average
performance increase of 137.65% over the next best model across all the
datasets
Data-driven prognosis method using hybrid deep recurrent neural network
Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction. © 2020 Elsevier B.V
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Reasoning is essential for the development of large knowledge graphs,
especially for completion, which aims to infer new triples based on existing
ones. Both rules and embeddings can be used for knowledge graph reasoning and
they have their own advantages and difficulties. Rule-based reasoning is
accurate and explainable but rule learning with searching over the graph always
suffers from efficiency due to huge search space. Embedding-based reasoning is
more scalable and efficient as the reasoning is conducted via computation
between embeddings, but it has difficulty learning good representations for
sparse entities because a good embedding relies heavily on data richness. Based
on this observation, in this paper we explore how embedding and rule learning
can be combined together and complement each other's difficulties with their
advantages. We propose a novel framework IterE iteratively learning embeddings
and rules, in which rules are learned from embeddings with proper pruning
strategy and embeddings are learned from existing triples and new triples
inferred by rules. Evaluations on embedding qualities of IterE show that rules
help improve the quality of sparse entity embeddings and their link prediction
results. We also evaluate the efficiency of rule learning and quality of rules
from IterE compared with AMIE+, showing that IterE is capable of generating
high quality rules more efficiently. Experiments show that iteratively learning
embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1
A deep attention based approach for predictive maintenance applications in IoT scenarios
Purpose: The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware. Design/methodology/approach: In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware. Findings: The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques. Research limitations/implications: A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time. Practical implications: The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature. Originality/value: The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness
TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction
Hard Disk Drive (HDD) failures in datacenters are costly - from catastrophic
data loss to a question of goodwill, stakeholders want to avoid it like the
plague. An important tool in proactively monitoring against HDD failure is
timely estimation of the Remaining Useful Life (RUL). To this end, the
Self-Monitoring, Analysis and Reporting Technology employed within HDDs
(S.M.A.R.T.) provide critical logs for long-term maintenance of the security
and dependability of these essential data storage devices. Data-driven
predictive models in the past have used these S.M.A.R.T. logs and CNN/RNN based
architectures heavily. However, they have suffered significantly in providing a
confidence interval around the predicted RUL values as well as in processing
very long sequences of logs. In addition, some of these approaches, such as
those based on LSTMs, are inherently slow to train and have tedious feature
engineering overheads. To overcome these challenges, in this work we propose a
novel transformer architecture - a Temporal-fusion Bi-encoder Self-attention
Transformer (TFBEST) for predicting failures in hard-drives. It is an
encoder-decoder based deep learning technique that enhances the context gained
from understanding health statistics sequences and predicts a sequence of the
number of days remaining before a disk potentially fails. In this paper, we
also provide a novel confidence margin statistic that can help manufacturers
replace a hard-drive within a time frame. Experiments on Seagate HDD data show
that our method significantly outperforms the state-of-the-art RUL prediction
methods during testing over the exhaustive 10-year data from Backblaze
(2013-present). Although validated on HDD failure prediction, the TFBEST
architecture is well-suited for other prognostics applications and may be
adapted for allied regression problems.Comment: 9 pages, 6 figures, 2 table
Review of Health Prognostics and Condition Monitoring of Electronic Components
To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' vis-a-vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted
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